The human brain is an exceedingly complex processing system, which integrates continual streams of incoming sensory input data with stored memories, uses the input data and memories in complex decision processes at both conscious and unconscious levels and, on the basis of these processes, generates observable behaviors by activation of its motor or movement control pathways and the muscles which these innervate.
In certain cases of traumatic injury or neurological disease, however, the brain is partially isolated from the periphery. Input data from certain senses are thus lost, at least for a portion of the body, as are many voluntary movements. Spinal cord injury is a well-known example. With spinal cord injury, the pathways that link higher motor centers in the brain with the spinal cord and that are used for control of voluntary movements can be functionally transected at the site of injury. As a result, the patient is paralyzed, and can no longer voluntarily activate muscles that are innervated by regions of the spinal cord below the level of the injury. Despite the injury to their long fibers, however, many of the cells in these higher brain regions that control voluntary movement will survive and can still be activated voluntarily to generate electric signals for controlling voluntary movement. By recording the electrical activities produced from these cells with implantable devices (e.g., a microwire electrode array or a microwire), signals generated by the cells can be “exteriorized” and used for the control of external prostheses, such as an assist robot or an artificial limb, or functional electrical stimulation paralyzed muscles.
Another example of such loss occurs in cases of amyotrophic lateral sclerosis (Lou Gehrig's Disease), in which the motor neurons that control muscles, as well as some of the brain cells that control these motor neurons, degenerate. In advanced stages of this disease, the patient might have completely intact senses and thought processes, but is “locked in,” so that neither movements nor behavioral expressions of any kind can be made. Providing these patients with some way of communicating with the external world would greatly enhance their quality of life.
In sum, there is a need to develop a system for monitoring and processing the electrical signals from neurons within the central nervous system, so that the brain's electrical activity can be “exteriorized” and used for the voluntary control of external prostheses or assist devices which are adapted to provide sensory feedback. In this way, damaged pathways are circumvented and some control of the environment can be restored, as well as impart to a patient the ability to interact with his or her environment. Because the electrical fields of small groups of neurons drop off rapidly with distance from the cells, a suitable system preferably includes surgically implanted electrodes or sensors, which can be placed in close proximity to the individual brain cells that generate command signals for voluntary movement.
Earlier attempts to utilize signals recorded directly from neurons for the express purpose of controlling an external actuator have, however, encountered a number of technical difficulties. One problem is how to obtain stable electrical signals of sufficient amplitude and temporal resolution for real-time control of an external device. Previous approaches have been used, but have been unsuccessful in this regard. Additionally, prior to the disclosure of the present invention, it was not possible for a patient to control the complex one- or three-dimensional complex trajectory of an external actuator via neural signals that was adapted to provide sensory feedback to the patient.
In recent years, small, multichannel, micromachined electrodes have been developed for use in neural recording. Given sufficient recording channel density, these electrodes offer a solution to the electrode/tissue movement problem described above. Another approach is to employ electrodes with larger exposed recording surfaces (in the range of 0.5 to 1.5 mm2 in surface area). These low impedance electrodes have lower noise characteristics than those with smaller tips, and can reliably record the activity of hundreds to thousands of single cortical neurons. Indeed, low level electroencephalographic (EEG) or other brain-derived neural signal information can even be recorded from the surface of the scalp. This approach can thus avoid the difficulty of different signal output levels caused by small movements between the electrodes and the selected cells encountered in the first approach.
The use of the signals recorded in the second approach, however, presents a major problem for actuator control. In such recordings, the desired control signals can be of very low amplitude and can be “buried” within, or confounded by, EEG potentials from neurons that are not involved in voluntary motor processes. Thus, averaging must be used over many movement attempts to extract a usable signal and the extracted signal cannot be employed to reproduce a time-varying arm trajectory. For this reason, this approach is less than desirable and perhaps not useful for real-time neural control of an external device.
Another problem, which occurs regardless of the electrode type used, is that neural signals can change over time for a variety of reasons, such as naturally occurring cell death, which occurs randomly throughout the brain in adults and learning processes, which might, over time, alter the quantitative relationship between a neuron's activity and the external parts of the body to which it contributes voluntary control.
Additionally, prior art apparatuses and methods have not addressed the closure of the “sensory loop” between the brain and the actuator. That is, even if a prior art apparatus or method could control an actuator by employing the brain signals of a subject, there is no sensory feedback from the actuator to the brain of the subject; the subject is not supplied with tactile and other sensory information acquired by the actuator. For example, if a patient directs an actuator to grasp an object and the actuator does grasp the object, the subject still does not know, for example, if the object is hard or soft, rough or smooth or hot or cold. This inability represents yet another limitation of prior art apparatuses and methods.
The methods and apparatuses described herein are adaptable to a variety of signals from the brain or central nervous system such as brain-derived electrical signals, acquired via microelectrode technologies from within the brain. The external devices can comprise any device that can be controlled by processed brain-derived electrical signals. These devices include, but are not limited to, artificial or prosthetic limbs; computer controlled, functional electrical stimulation of muscles of paralyzed individuals for the restoration of movement; robots or robotics components; computers or computer displays; or the teleoperation of robots and machines in hostile environments.
What is needed, therefore, is a closed loop brain-machine interface that can translate neural signals in the brain of a subject into movement of an external device, the external device adapted to provide sensory feedback to the subject. The present invention solves these and other problems associated with methods and apparatuses for obtaining signals directly from the brain or central nervous system, for processing and utilizing these signals to control one or more external devices, such as an actuator, and for providing a subject with sensory feedback from the external devices.